AI Security Explained

What is AI security?

AI, or Artificial Intelligence, refers to technologies that enable computers to perform a range of advanced processes, such as seeing, understanding, and analyzing data, as well as translating spoken and written language.

The integration of AI is a present-day reality, fundamentally transforming how organizations operate and defend themselves. Its practical applications have moved beyond theoretical concepts into critical daily business functions. For instance, Optical Character Recognition (OCR) uses AI to convert images into structured, manipulable data, turning unstructured information into a business-ready format. On a more complex level, machine learning (ML) algorithms are the core technology powering autonomous vehicles.  Furthermore, deep learning (DL) refers to a subset of machine learning (ML) and serves as the foundation of many modern AI systems. It enables the automation of intricate and repetitive business processes, driving efficiency across organizations.

In cybersecurity, these capabilities are harnessed for defense. One of the main applications is machine learning, which is defined as "the use and development of computer systems that are capable of learning and adapting without explicit instructions by leveraging statistical and algorithmic models to draw inferences."

Current AI models simulate human intelligence and behavior, creating automated capabilities that complement human efforts. For instance, AI-trained models, supported by security tools, can detect intrusions within seconds and take automated action as defined by security policies. This use of AI has significantly transformed the digital landscape, leading companies to become dependent on technologies like machine learning, AI, and big data. However, this dependency has a dual edge, as it has subsequently led to a rise in cybercrimes that also leverage AI models.

The Two Key Aspects of AI Security

AI security is defined by two distinct but interconnected aspects:

1. Using AI for Security: This involves leveraging AI to enhance an organization's security posture. This can take the form of utilizing AI to automate key procedures such as monitoring, prevention, and remediation during an incident response.

2. Securing AI: This refers to the practice of protecting the AI technology itself from malicious actions that could cause the models to provide inaccurate or biased information, which would be harmful to an organization for example, the poisoning of LLM models.

How AI is Utilized in Cybersecurity

Organizations can incorporate and utilize AI for cybersecurity in a plethora of ways. The most common application is the use of machine learning and deep learning to help ingest and analyze large amounts of data, such as security data traffic trends, browser activities, and network activities.

The data processed by these models is used to help establish a baseline a normal or expected state of behavior and performance. This baseline is crucial for monitoring, investigating, and responding to incidents. The aggregation and learning capabilities of AI allow for the quick differentiation between normal baseline activity and malicious activities.

Machine learning leverages and is excellent for classification and predictive processes, an example of said processes could be email classification where an algorithm using indicators such as email header, text and email links to determine if an email is malicious.

Deep learning(DL) a subset of Machine learning utilizes neural networks, ideal for complex pattern recognition from unstructured data link raw network traffic to help identify malicious activities or statically analyzing a file.

Generative AI create new content, which can help augment or assist human efforts such as creation of detection rules, analysis of an excel file and production of realistic training data.

Generative AI and Its Security Implications

What is Generative AI?

Generative AI is a type of AI model that can create original text, images, videos, and audio in response to a user's input/request. This technology relies on machine learning and deep learning models to simulate the learning and decision-making process of a human brain. The models work by identifying and encoding patterns and relationships in huge amounts of data, using this information to understand and respond to natural language.

The power of AI tools, including generative models, is significant. Research, such as the IBM Cost of a Data Breach Report, suggests that the introduction of AI tools significantly improves threat detection and incident response.

The Challenge of Shadow AI

A major risk associated with the ease of access to generative AI is the emergence of Shadow AI. To understand this, one must first understand Shadow IT "the use of unauthorized software, hardware, or services within an organization without the IT department's knowledge or approval". This creates security blind spots and increases and threat landscape.

Shadow AI is a direct evolution of this problem. It refers specifically to the "unsanctioned use of artificial intelligence tools and platforms by employees. This is often driven by a desire to automate tasks and boost productivity but occurs outside the governance of the IT or security teams". A common and high-risk example is when an employee uploads sensitive company files such as financial reports, proprietary code, or customer data to a public generative AI model like ChatGPT to summarize, analyze, or reformat the information.

This action, while perhaps well-intentioned, bypasses all organizational data security controls and poses a severe threat to data confidentiality and compliance.

The prevalence of this issue is growing. From 2023 to 2024, the use of generative AI saw a substantial rise, and a concerning number of employees acknowledge sharing sensitive information with AI models without permission from superiors or IT personnel.

Risk of Shadow AI

The use of unsanctioned AI poses several critical risks to organizations:

1. Data Breaches

• Lack of oversight can inadvertently expose sensitive information, leading to a privacy breach.
• Inadvertent Public Disclosure: An AI model might reproduce confidential data in response to a user request.
• Intellectual Property (IP) Theft: Sharing proprietary code or trade secrets with an external AI service risks the loss of control over that IP, effectively granting the AI vendor a license to use it

2. Non-Compliance

• In many industries compliance is a non-negotiable matter since it helps protect organizations from underlying threats in a digital landscape.
• Sharing sensitive Personal Identifiable Information (PII) with public models like ChatGPT or Claude can lead to serious compliance issues with data protection regulations like the GDPR.
• The penalties for non-compliance are substantial and can reach significant financial amounts

The Benefits of AI Security

When properly governed and implemented, AI security offers powerful advantages:

• Faster Incident Response: Shortens the time needed to conduct incident response procedures, allowing organizations to address threats more quickly and reduce potential damage.
• Enhanced Threat Detection: AI models can digest large sets of data in real-time, providing critical information promptly. This data also serves as training material to continuously improve the model's accuracy.
• Greater Operational Efficiency: Automating tasks with AI streamlines security operations and reduces costs. This optimization helps reduce human error and frees up time for more sensitive projects.
• Proactive Approach: AI security enables a proactive stance by using historical data to predict and guard against future threats.
• Ability to Scale: AI security models can scale to protect large and complex IT environments and are designed to integrate with existing tools like SIEM platforms to enhance real-time threat intelligence and automated remediation.